Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real Time Facial Expression Recognition with Adaboost
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Tagging video contents with positive/negative interest based on user's facial expression
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
| Hi-index | 0.00 |
One of the most crucial techniques associated with Computer Vision is technology that deals with facial recognition, especially, the automatic estimation of facial expressions. However, in real-time facial expression recognition, when a face turns sideways, the expressional feature extraction becomes difficult as the view of camera changes and recognition accuracy degrades significantly. Therefore, quite many conventional methods are proposed, which are based on static images or limited to situations in which the face is viewed from the front. In this paper, a method that uses Look-Up-Table (LUT) AdaBoost combining with the three-dimensional average face is proposed to solve the problem mentioned above. In order to evaluate the proposed method, the experiment compared with the conventional method was executed. These approaches show promising results and very good success rates. This paper covers several methods that can improve results by making the system more robust.